An end-to-end geospatial analytics project that optimizes delivery routes by clustering retail shops around fulfillment centers to reduce travel distance, improve vehicle utilization, and lower logistics costs.
Last-mile delivery is often the most expensive stage of the supply chain.
When retailers are assigned inefficient delivery routes, organizations experience:
- Increased fuel consumption
- Low vehicle utilization
- Longer delivery times
- Higher operational costs
This project demonstrates how geospatial analytics and routing optimization can improve delivery efficiency through intelligent spatial clustering.
This project was developed as part of a logistics optimization case study involving retail distribution.
Using geospatial analysis techniques, retail shops were grouped according to their proximity to fulfillment centers. Distance calculations and clustering algorithms were then applied to create more efficient delivery routes while maximizing vehicle capacity.
The final output includes optimized geographic clusters and interactive route visualizations that support operational planning and decision-making.
The solution helps logistics teams:
- Reduce transportation costs
- Improve vehicle utilization
- Minimize delivery distance
- Support warehouse and fulfillment planning
- Improve overall distribution efficiency
The project aims to:
- Analyze spatial relationships between shops and fulfillment centers
- Cluster retailers into efficient delivery groups
- Calculate delivery distances
- Optimize route assignments
- Visualize delivery clusters geographically
- Support data-driven logistics planning
- Geospatial clustering of retail locations
- Distance-based route optimization
- Delivery network visualization
- Vehicle utilization optimization
- Interactive map outputs
- Operational decision support
The workflow followed these stages:
Retail Locations
↓
Data Cleaning
↓
Distance Calculation
↓
Spatial Clustering
↓
Route Optimization
↓
Map Visualization
↓
Business Insights
The optimized routing strategy demonstrated:
- Reduced travel distance
- Lower fuel consumption
- Better vehicle utilization
- Fewer delivery trips
- Improved operational efficiency
- Python
- Pandas
- NumPy
- Scikit-learn
- Folium
- Geopy
- Matplotlib
- Jupyter Notebook
dynamic-routing/
│
├── data/
│ ├── dataset.csv
│ ├── Dynamic_routedata.xlsx
│ └── territories_centroids.csv
│
├── notebook/
│ └── Datasciencecasestudy.ipynb
│
├── images/
│ ├── route_map.png
│ └── clusters.png
│
├── docs/
│ └── Case Study.pdf
│
├── presentation/
│ └── Company Presentation.pptx
│
├── dynamic_routing.html
├── README.md
├── LICENSE
└── .gitignore
This project strengthened my understanding of geospatial analytics, spatial clustering, logistics optimization, distance-based routing, and transforming geographic data into actionable business insights. It also improved my ability to communicate technical findings through maps and interactive visualizations for operational decision-making.
Potential enhancements include:
- Real-time GPS integration
- Traffic-aware routing
- Vehicle capacity optimization
- Multiple warehouse optimization
- Dynamic route recalculation
- Integration with Google Maps API
The datasets used in this project have been anonymized for demonstration purposes.
Any company names, customer locations, and operational information have been modified to protect confidentiality.
Released under the MIT License.